Method and apparatus for evaluating relevance of keyword to asset price

ABSTRACT

Methods and apparatus for evaluating relevance of keyword to asset price are provided, one of methods comprises, collecting text content posted on a first date through the Internet, generating one or more daily keywords of the first date by extracting a keyword from each piece of the text content, generating daily appearance frequency information of each daily keyword of the first date, and determining an asset corresponding to each daily keyword by comparing the generated daily appearance frequency information of each daily keyword with daily price information of each pre-registered asset.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of Korean Patent Application No.10-2015-0160168, filed on Nov. 16, 2015, in the Korean IntellectualProperty Office, the disclosure of which is incorporated herein byreference in its entirety.

BACKGROUND

1. Field

The present inventive concept relates to a method and apparatus forevaluating the relevance of a keyword to an asset price, and moreparticularly, to a method and apparatus for evaluating a daily keywordgenerated automatically using text content to an asset price.

2. Description of the Related Art

With the development of information technology, various issues of thesociety are being widely shared over the Internet. Economic issues suchas corporate performance announcements and development plans in aspecific area affect stock prices of individual companies and realestate price fluctuations. In addition to the economic issues, politicaland social issues such as international relations and incidents have awide-reaching effect on the economy. For example, a particular socialevent can reduce consumer confidence, thereby affecting the economy.

As such various issues of the society are reproduced on an enlargedscale in the Internet space, they are becoming more influential.Therefore, it is required to analyze the influence of an issue on assetvalue fluctuations when an asset investment or sale plan of anindividual is established.

However, the service of predicting an issue that will affect the priceof an asset being held or to be invested in is not yet available. Inaddition, the service of analyzing the specific influence of each issueon an asset and providing investment guidance on the asset to anindividual is not yet available.

SUMMARY

Aspects of the inventive concept provide a method and apparatus foranalyzing the influence of various issues on an asset price.

Aspects of the inventive concept also provide a method and apparatus forextracting keywords indicating various issues from text content whichcontains the various issues.

Aspects of the inventive concept also provide a method and apparatus forautomatically determining the influence of a keyword on an asset.

Aspects of the inventive concept also provide a method and apparatus forproviding investment guidance to a user by analyzing the influence of akeyword on the price of an asset.

Specifically, aspects of the inventive concept also provide a method andapparatus for predicting the influence of a keyword on an asset based onpre-collected relevance information of the keyword to the asset.

Aspects of the inventive concept also provide a method and apparatus forproviding a keyword related to an asset being held or targeted by auser.

Specifically, aspects of the inventive concept also provide a method andapparatus for providing a keyword related to an asset being held ortargeted by a user to offer the user an opportunity to cope with asituation where the keyword becomes an issue.

However, aspects of the inventive concept are not restricted to the oneset forth herein. The above and other aspects of the inventive conceptwill become more apparent to one of ordinary skill in the art to whichthe inventive concept pertains by referencing the detailed descriptionof the inventive concept given below.

According to one exemplary embodiment of the present invention, a methodof automatically generating a daily keyword using text content by aservice server is provided, the method comprises collecting text contentposted on a first date through the Internet, extracting a keyword fromeach piece of the text content and forming a keyword pool of theextracted keywords of the first date, and generating one or more dailykeywords of the first date using the result of comparing the keywordpool of the first date and a keyword pool of a second date.

According to the exemplary embodiment, wherein the generating of thedaily keywords of the first date comprises determining a first timewindow based on the first date, comparing the keyword pool of the firstdate with a keyword pool of at least one date included in the first timewindow, and generating one or more daily keywords of the first dateusing the comparison result.

According to the exemplary embodiment, wherein the generating of thedaily keywords of the first date comprises determining a first timewindow based on the first date, determining whether a new keyword postedmore than a predetermined number of times is included in the keywords ofthe keyword pool of the first date and generating one or more dailykeywords including the new keyword when the new keyword is included inthe keywords of the keyword pool of the first date, wherein the newkeyword is not included in a daily keyword pool of at least one otherdate within the first time window.

According to the exemplary embodiment wherein the generating of thedaily keywords of the first date comprises determining a first timewindow and a second time window based on the first date, determiningwhether to remove each of the keywords included in the keyword pool ofthe first date based on a ratio of the number of times that each of thekeywords was posted within the second time window and the number oftimes that each of the keywords was posted within the first time windowand generating one or more daily keywords of the first date based on thedetermination result, wherein the second time window comprises moredates than the first time window.

According to the exemplary embodiment, wherein the generating of thedaily keywords of the first date comprises identifying sources of piecesof the collected text content which comprise the daily keywords of thefirst date and prioritizing the daily keywords of the first date basedon the identified sources.

According to the exemplary embodiment, wherein when one of the dailykeywords of the first date has different sources, the generating of thedaily keywords of the first date comprises determining the keyword to bedifferent keywords according to attributes of each of the differentsources.

According to the exemplary embodiment, wherein the generating of thedaily keywords of the first date comprises identifying sources of piecesof the collected text content which comprise the daily keywords of thefirst date and matching the daily keywords of the first date with assetsbased on the identified sources.

According to another exemplary embodiment of the present invention, amethod of evaluating the relevance of a keyword to an asset price by aservice server is provided, the method comprises collecting text contentposted on a first date through the Internet, generating one or moredaily keywords of the first date by extracting a keyword from each pieceof the text content, generating daily appearance frequency informationof each daily keyword of the first date, and determining an assetcorresponding to each daily keyword by comparing the generated dailyappearance frequency information of each daily keyword with daily priceinformation of each pre-registered asset.

According to yet another exemplary embodiment, wherein the determiningof the asset corresponding to each daily keyword comprises identifyingan asset whose daily price change during a second period is equal to orgreater than a threshold value in response to the daily appearancefrequency of each daily keyword during a first period and determiningthe identified asset to be an asset corresponding to each daily keyword.

According to yet another exemplary embodiment, wherein the price changecomprises an absolute value of the price change.

According to yet another exemplary embodiment, wherein the determiningof the asset corresponding to each daily keyword comprises determiningwhether the same keyword as a daily keyword of the first date isincluded in one or more daily keywords of a second date, monitoringdaily price information of an asset determined to correspond to the samekeyword when the same keyword is included in the daily keywords of thesecond date and determining relevance information of the same keyword tothe determined asset based on the monitoring result, wherein the dailyprice information of the determined asset is daily price information ofthe determined asset during a preset period of time based on the seconddate.

According to yet another exemplary embodiment, wherein the determiningof the relevance information comprises updating the relevanceinformation of the same keyword to the determined asset when it ismonitored that the price change of the determined asset during thepreset period of time is equal to or greater than a threshold value.

According to yet another exemplary embodiment, wherein the determiningof the asset corresponding to each daily keyword comprises, when aplurality of daily keywords of the first date which correspond to thesame asset exist, determining whether any one of the plurality of thedaily keywords is included in daily keywords of the second date,monitoring daily price information of the asset determined to correspondto the any one of the keywords when the any one of the daily keywords isincluded in the daily keywords of the second date, and determiningrelevance information of the any one of the daily keywords to thedetermined asset based on the monitoring result, wherein the daily priceinformation of the determined asset is daily price information of thedetermined asset during a period of time within a preset range from thesecond date.

According to yet another exemplary embodiment, wherein the determined ofthe asset corresponding to each daily keyword comprises when a pluralityof daily keywords of the first date which correspond to the same assetexist, determining whether the plurality of daily keywords are includedin daily keywords of the second date, monitoring daily price informationof the asset determined to correspond to the plurality of daily keywordswhen the daily keywords are included in the daily keywords of the seconddate, and determining relevance information of the plurality of dailykeywords to the determined asset based on the monitoring result, whereinthe daily price information of the determined asset is daily priceinformation of the determined asset during a period of time within apreset range from the second date.

According to other exemplary embodiment of the present invention, anapparatus for evaluating the relevance of a keyword to an asset price isprovided, the apparatus comprises one or more processors, a memory whichloads a computer program executed by the processors, a storage unitwhich stores daily price information of each pre-registered asset anddaily keywords generated by the execution of the computer program, and anetwork interface which transmits the daily keywords,wherein thecomputer program comprises, an operation of collecting text contentposted on a first date through the Internet, an operation of generatingone or more daily keywords of the first date by extracting a keywordfrom each piece of the text content, an operation of generating dailyappearance frequency information of each daily keyword of the firstdate, and an operation of determining an asset corresponding to eachdaily keyword by comparing the generated daily appearance frequencyinformation of each daily keyword with the daily price information ofeach pre-registered asset.

According to the other exemplary embodiment, wherein the operation ofdetermining the asset corresponding to each daily keyword comprises anoperation of identifying an asset whose daily price change during asecond period is equal to or greater than a threshold value in responseto the daily appearance frequency of each daily keyword during a firstperiod and an operation of determining the identified asset to be anasset corresponding to each daily keyword.

According to the other exemplary embodiment, wherein the computerprogram further comprises an operation of generating one or more dailykeywords of a second date, and an operation of determining whether thesame keyword as any one of the daily keywords of the first date isincluded in the daily keywords of the second date.

According to the other exemplary embodiment, wherein the operation ofdetermining the asset corresponding to each daily keyword comprises anoperation of measuring a time gap between the first period and thesecond period and an operation of storing the result of measuring thetime gap as relevance information of each daily keyword to thecorresponding asset, wherein the computer program further comprises anoperation of transmitting investment guidance on the corresponding assetto a user terminal based on the relevance information when it isdetermined that the same keyword as the any one of the daily keywords ofthe first date is included in the daily keywords of the second date.

According to the other exemplary embodiment, wherein the operation ofdetermining the asset corresponding to each daily keyword comprises anoperation of determining which of the first period and the second periodprecedes the other period and an operation of storing the determinationresult as relevance information of each daily keyword to thecorresponding asset, wherein the computer program further comprises anoperation of transmitting investment guidance on the corresponding assetto a user terminal based on the relevance information when it isdetermined that the same keyword as the any one of the daily keywords ofthe first date is included in the daily keywords of the second date.

According to the other exemplary embodiment, wherein the operation ofdetermining the asset corresponding to each daily keyword comprises anoperation of storing the second period as relevance information of eachdaily keyword to the corresponding asset, wherein the computer programfurther comprises an operation of transmitting investment guidance onthe corresponding asset to a user terminal based on the relevanceinformation when it is determined that the same keyword as the any oneof the daily keywords of the first date is included in the dailykeywords of the second date.

According to the other exemplary embodiment, wherein the operation ofdetermining the asset corresponding to each daily keyword comprises anoperation of storing a daily price change which is equal to or greaterthan the threshold value as relevance information of each daily keywordto the corresponding asset, wherein the computer program furthercomprises an operation of transmitting investment guidance on thecorresponding asset to a user terminal based on the relevanceinformation when it is determined that the same keyword as the any oneof the daily keywords of the first date is included in the dailykeywords of the second date.

According to the other exemplary embodiment, wherein the computerprogram further comprises an operation of, when receiving informationabout a selected daily keyword from a user terminal, transmittinginformation about an asset corresponding to the selected daily keywordto the user terminal.

According to the other exemplary embodiment, wherein the computerprogram further comprises an operation of, when receiving informationabout an asset selected by a user from a user terminal, extracting akeyword corresponding to the selected asset, and an operation oftransmitting the keyword corresponding to the selected asset to the userterminal.

According to the other exemplary embodiment, wherein the storage unitstores an asset selected by a user in advance among the pre-registeredassets, and the computer program further comprises an operation ofgenerating one or more daily keywords of a second date, an operation ofdetermining whether the same keyword as any one of the daily keywords ofthe first date is included in the daily keywords of the second date, andan operation of transmitting a keyword corresponding to the assetselected by the user to a user terminal when it is determined that thesame keyword as the any one of the daily keywords of the first date isincluded in the daily keywords of the second date.

According to other exemplary embodiment of the present invention,another apparatus for evaluating the relevance of a keyword to an assetprice is provided, the another apparatus comprises one or moreprocessors, a memory which loads a computer program executed by theprocessors, and a storage unit which stores daily price information ofeach pre-registered asset and daily keywords generated by the executionof the computer program, wherein the computer program comprises, anoperation of identifying an asset whose daily price change during afirst period is equal to or greater than a threshold value among thepre-registered assets, an operation of collecting text content posted ona first date through the Internet, an operation of generating one ormore daily keywords of the first date by extracting a keyword from eachpiece of the text content, an operation of detecting daily appearancefrequency of each daily keyword of the first date during a secondperiod, an operation of extracting a keyword whose daily appearancefrequency during the second period corresponds to the daily price changeof the identified asset during the first period from the daily keywordsof the first date, and an operation of determining the extracted keywordto be a keyword corresponding to the identified asset.

According to the other exemplary embodiment, the apparatus furthercomprises a network interface which transmits the determined keyword,wherein the computer program comprises an operation of detecting a pricechange of the identified asset which is equal to or greater than thethreshold value during a third period, an operation of identifying anasset corresponding to the determined keyword among the pre-registeredassets, and an operation of transmitting investment guidance on theidentified asset to a user terminal.

According to other exemplary embodiment of the present invention, amethod of displaying asset information matched with text content by auser terminal is provided, the method comprises displaying text contentin a first area of a display unit of the user terminal, extracting oneor more keywords from the text content, extracting an asset matched witheach of the extracted keywords from pre-registered assets, anddisplaying price information of an asset which has been extracted apreset number of times or more in a second area different from the firstarea when the asset which has been extracted the preset number of timesor more is included in the extracted assets.

According to the other exemplary embodiment, wherein the asset matchedwith each of the extracted keywords comprises an asset whose daily pricechange during a second period is equal to or greater than a thresholdvalue in response to daily appearance frequency of each of the extractedkeywords during a first period, and the price information comprisesprediction information about the price of the asset which has beenextracted the preset number of times or more, wherein the predictioninformation about the price of the asset which has been extracted thepreset number of times or more is determined based on relevanceinformation of each of the extracted keywords to the asset which hasbeen extracted the preset number of times or more.

According to the other exemplary embodiment, wherein the extracting ofthe asset matched with each of the extracted keywords comprises, whenthe asset which has been extracted the preset number of times or more isincluded in the extracted assets, matching the asset which has beenextracted the preset number of times or more with the text content.

According to other exemplary embodiment of the present invention,another method of displaying asset information matched with text contentby a user terminal is provided, the another method comprises displayinginformation about an asset in a first area of a display unit of a userterminal, displaying a list of pieces of text content matched with theasset in a second area different from the first area, and when any oneof the pieces of the text content is selected, displaying the selectedpiece of the text content, wherein each piece of the text contentmatched with the asset comprises at least one keyword matched with theasset.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of the embodiments, taken inconjunction with the accompanying drawings in which:

FIG. 1 is a conceptual diagram illustrating the relevance of a keywordto an asset price, which is referred to in some embodiments;

FIG. 2 illustrates a system for evaluating the relevance of a keyword toan asset price according to an embodiment;

FIG. 3 is a block diagram of a service server according to anembodiment;

FIG. 4 is a flowchart illustrating a method of automatically generatinga keyword using text content according to an embodiment;

FIG. 5 illustrates keyword pools which are referred to in someembodiments;

FIG. 6 illustrates a daily keyword which is referred to in someembodiments;

FIG. 7 illustrates a first time window for determining a daily keyword,which is referred to in some embodiments;

FIG. 8 illustrates a second time window for determining a daily keyword,which is referred to in some embodiments;

FIGS. 9 and 10 illustrate a keyword refinement process which is referredto in some embodiments;

FIG. 11 illustrates priority rankings of daily keywords according tosource, which are referred to in some embodiments;

FIG. 12 illustrates assets matched with keywords, which are referred toin some embodiments;

FIG. 13 is a flowchart illustrating a method of evaluating the relevanceof a keyword to an asset price according to an embodiment;

FIG. 14 illustrates an example process of determining an assetcorresponding to a keyword, which is referred to in some embodiments.

FIG. 15 illustrates another example process of determining an assetcorresponding to a keyword, which is referred to in some embodiments;

FIG. 16 illustrates the influence of keywords on an asset, which isreferred to in some embodiments;

FIG. 17 illustrates the difference between a time when a keyword isgenerated and a time when the price of an asset is changed, which isreferred to in some embodiments;

FIG. 18 illustrates a period of time during which a keyword affects anasset, which is referred to in some embodiments;

FIG. 19 illustrates an example process of identifying a keyword thataffects an asset among a plurality of keywords, which is referred to insome embodiments;

FIG. 20 illustrates an asset affected by a plurality of keywords, whichis referred to in some embodiments;

FIG. 21 illustrates relevance information of keywords to assets, whichis referred to in some embodiments;

FIG. 22 illustrates relevance indices of keywords to assets, which arereferred to in some embodiments;

FIG. 23 illustrates an example graphic user interface (GUI) forproviding daily keywords, according to an embodiment;

FIG. 24 illustrates an investment guidance interface based on a timewhen a keyword affects the price of an asset, which is referred to insome embodiments;

FIG. 25 illustrates an investment guidance interface based on the degreeof influence of a keyword on the price of an asset, which is referred toin some embodiments;

FIG. 26 illustrates a daily keyword corresponding to an asset accordingto an embodiment;

FIG. 27 is a flowchart illustrating a method of extracting a dailykeyword corresponding to a price change of an asset according to anembodiment;

FIG. 28 illustrates a service of, when the price of an asset is changed,recommending another asset according to an embodiment; and

FIG. 29 is a conceptual diagram illustrating the matching relationshipbetween text content, keywords and assets according to an embodiment.

DETAILED DESCRIPTION

FIG. 1 is a conceptual diagram illustrating the relevance of a keywordto an asset price, which is referred to in some embodiments. Texts aredistributed through various web pages such as blogs, Internet news,messengers and social networking service (SNS). The content of each textincludes various issues, and these issues can affect the values ofvarious types of assets.

Referring to FIG. 1, if text 1 is a text posted on a stock informationblog, the content of text 1 may include stock information. If issue 1included in the content of text 1 is a stock forecast for company A, itcan affect the stock value of the company. In addition, text 2 may be atext posted on a web page of Internet news, and the content of text 2may include a forecast for the domestic real estate market as an issue.In this case, if issue 2 is information about real estate prices of areaB, it can affect the real estate prices.

An issue can be distributed over the Internet in the form of keywordsindicating the issue. In the above example, issue 1 can be expressed bykeywords such as “A,” “company A,” “stock price of company A,” etc. Inaddition, issue 2 can be expressed by keywords such as ‘B,” “real estateB,” “price of B,” etc. The configuration and operation of a system forevaluating the relevance of a keyword to an asset price according to anembodiment will now be described with reference to the above-describedtext content and keywords.

FIG. 2 illustrates a system for evaluating the relevance of a keyword toan asset price according to an embodiment. For ease of description, thesystem for evaluating the relevance of a keyword to an asset price willhereinafter be referred to as a system. Referring to FIG. 2, the systemmay include a service server 100, user terminals 200, and an externaldevice 300.

The service server 100, the user terminals 200 and the external device300 are computing devices connected to each other through the Internet.The service server 100 may be a service device which stores variousinformation and one or more programs for implementing embodiments of theinventive concept. Each of the user terminals 200 may be any one of afixed computing device, such as a server device or a desktop PC, and amobile computing device such as a notebook computer, a smartphone or atablet PC. In addition, the external device 300 may be a server devicewhich stores text content available on the Internet. The external device300 may also be a server device which stores information about assetsand price information of the assets. For example, the external device300 may be a stock information server of a stock exchange which providesstock price information.

In the system according to the embodiment, the service server 100 maycollect text content posted on a first date from the external device 300through the Internet. To this end, the service server 100 may store aweb crawler which automatically searches web pages. For example, thefirst date may be the very date on which the service server 100 performscrawling. In this case, the service server 100 may collect text contentposted on the Internet until a preset time of that date.

The service server 100 may extract a keyword from each piece of textcontent collected by the crawling of the web crawler. At this time,various algorithms may be used for keyword extraction. The serviceserver 100 may store at least one program for algorithms used in keywordextraction. For example, the service server 100 may use a latentdirichlet allocation (LDA) algorithm. The service server 100 maydetermine the topic of the text content and extract keywords having highrelevance to the topic using the LDA algorithm.

After extracting a keyword from each piece of the collected textcontent, the service server 100 may combine the keywords extracted fromvarious sources. In so doing, the service server 100 may form a keywordpool of the extracted keywords of the first date. In the same way, theservice server 100 may form a keyword pool of a second date. Here, thesecond date is a different date from the first date and may be anadjacent date within a preset range from the first date.

The service server 100 may compare the keyword pool of the first datewith the keyword pool of the second date and generate one or more dailykeywords of the first date using the comparison result.

The service server 100 may provide the generated daily keywords to theuser terminals 200. In addition, the service server 100 may providevarious services to each of the user terminals 200 using the dailykeywords. For example, the service server 100 may provide an investmentguidance service to the user terminals 200 using the daily keywords.

According to an embodiment, the service server 100 may generate one ormore daily keywords of the first date based on text content collectedfrom the external device 300 through the Internet. In addition, theservice server 100 may generate daily appearance frequency informationof each daily keyword of the first date.

The service server 100 may compare the generated daily appearancefrequency information of each daily keyword with daily price informationof each pre-registered asset. Accordingly, the service server 100 maydetermine which of the pre-registered assets corresponds to each dailykeyword.

The service server 100 may transmit information about the daily keywordsof the first date and an asset corresponding to each daily keyword tothe user terminals 200.

FIG. 3 is a block diagram of a service server 100 according to anembodiment.

Referring to FIG. 3, the service server 100 may include a processor 101,a network interface 102, a memory 103, and a storage unit 104.

The processor 101 controls the overall operation of each component ofthe service server 100. The processor 101 may be a central processingunit (CPU), a microprocessor unit (MPU), a microcontroller unit (MCU),or any processor well known in the art to which the inventive conceptpertains. In addition, the processor 101 may perform an operation on atleast one application or program for executing methods according toembodiments. The service server 100 may include one or more processors.

The network interface 102 supports wired and wireless Internetcommunication of the service server 100. The network interface 102 mayalso support various communication methods other than the Internetcommunication. To this end, the network interface 102 may includevarious communication modules.

The network interface 102 may collect text content from the externaldevice 300 through the Internet. In addition, the network interface 102may transmit or receive information about keywords and assets to or fromthe user terminals 200.

The memory 103 stores various data, commands and/or information. Inaddition, the memory 103 may store one or more programs for loading oneor more programs 105 from the storage unit 104 to perform a method ofautomatically generating a daily keyword using text content and a methodof evaluating the relevance of a keyword to an asset price according toembodiments. In FIG. 3, a random access memory (RAM) is illustrated asan example of the memory 103.

The storage unit 104 may non-temporarily store data received from theexternal device 300. The storage unit 104 may be a nonvolatile memorysuch as a read only memory (ROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM) or a flash memory, ahard disk, a removable disk, or any computer-readable recording mediumwell known in the art to which the inventive concept pertains.

The storage unit 104 may store one or more programs 105 for performingmethods according to embodiments. In FIG. 3, asset management softwareis illustrated as an example of the programs 105.

A database (DB) 106 of keyword pools and daily keywords may be installedin the storage unit 104. In addition, a DB 107 of pre-registered assetsand an asset corresponding to each keyword may be installed in thestorage unit 104.

Although not illustrated in the drawing, the service server 100 mayfurther include an input unit for inputting various settings andinformation and an output unit for displaying information. The inputunit and the output unit may respectively include any input medium andany output medium well known in the art to which the inventive conceptpertains.

In the present specification, the service server 100 may be referred toas an apparatus for evaluating the relevance of a keyword to an assetprice because it performs a method of evaluating the relevance of akeyword to an asset price. In addition, the service server 100 may bereferred to as an apparatus for automatically generating a daily keywordbecause it performs a method of automatically generating a daily keywordusing text content. The service server 100 may also simply be shortenedto an apparatus.

It will hereinafter be assumed that methods according to embodiments areperformed by the service server 100.

Based on the above description of FIGS. 1 through 3, embodiments of theinventive concept will hereinafter be described according to each methodperformed by the service server 100. The embodiments described belowshould not necessarily be implemented separately but can be implementedin combination. In addition, it should be noted that the embodimentsdescribed below can be implemented in combination with the embodimentsdescribed above with reference to FIGS. 1 through 3.

Method of Automatically Generating a Daily Keyword using Text Content

According to an embodiment, the apparatus 100 for automaticallygenerating a daily keyword may perform a method of automaticallygenerating a daily keyword using text content. The method ofautomatically generating a daily keyword using text content will now bedescribed in detail with reference to FIGS. 4 through 12.

FIG. 4 is a flowchart illustrating a method of automatically generatinga keyword using text content according to an embodiment. Referring toFIG. 4, the apparatus 100 may collect text content of a first datethrough the Internet (operation S10). The apparatus 100 may extract akeyword from each piece of the text content and form a keyword pool ofthe extracted keywords of the first date (operation S20). The method ofcollecting text content and extracting keywords has been described abovewith reference to FIG. 1. To generate one or more daily keywords of thefirst date, the apparatus 100 may use text content collected until apreset time of the first date.

FIG. 5 illustrates keyword pools which are referred to in someembodiments. In addition, FIG. 6 illustrates a daily keyword which isreferred to in some embodiments.

Referring to FIG. 5, the apparatus 100 may form a keyword pool 501 byextracting keywords from text content posted on date D1. In addition,the apparatus 100 may form a keyword pool 502 and a keyword pool 503 byextracting keywords from text content posted on date D2 and text contentposted on date D3, respectively. Each of the keyword pools 501 through503 may include a preset number of keywords. In addition, each of thekeyword pools 501 through 503 may include keywords listed in order oflargest number of extractions. The keyword pools 501 through 503 may bestored in the storage unit 104 of the apparatus 100.

The apparatus 100 may compare the keyword pool of the first date and akeyword pool of a second date (operation S30). In the above example, theapparatus 100 may compare the keyword pool 501, the keyword pool 502,and the keyword pool 503. Using the comparison result, the apparatus 100may generate one or more daily keywords of the first date (operationS40).

Here, a daily keyword is a keyword that appears in text contentcollected on a specific date with a frequency distinguished fromfrequencies on other dates. That is, a daily keyword of the first dateis a keyword that distinguishes the first date from other dates. Forexample, if a particular issue occurs on the first date, the Internetsearch for the particular issue may increase rapidly, and the issue maybe mentioned in many web pages. In this case, many keywords indicatingthe issue may be included in text content collected by the apparatus100. Accordingly, the apparatus 100 may form a keyword pool of thekeywords indicating the issue. The apparatus 100 may generate a keywordexisting in high proportion in the keyword pool as a daily keyword ofthe first date.

In FIG. 6, a graph 600 of the time-series appearance frequency ofkeyword 1 KW1 in text content is illustrated. It is assumed that keyword1 KW1 is “North Korea's nuclear test” included in the keyword pools 501through 503 of FIG. 5. In addition, it is assumed that date D2 is t1 inFIG. 6.

Referring to FIGS. 5 and 6, the appearance frequency of keyword 1 KW1 ishigher on date D2 than on other dates. In FIG. 5, the appearancefrequency of keyword 1 KW1 ranked 17^(th) on date D1, 1^(st) on date D2,and 25^(th) on date D3. That is, the keyword “North Korea's nucleartest” is a keyword that distinguishes date D2 from date D1 and date D3.

In operation S30, the apparatus 100 may compare the appearance frequencyof each keyword included in the keyword pool of the first date with theappearance frequency of each keyword included in the keyword pool of thesecond date. In particular, the apparatus 100 may determine a keywordwhose appearance frequency on the first date is different fromappearance frequencies on other dates by a threshold value or more to bea daily keyword of the first date. It is assumed that the appearancefrequency of keyword 1 KW1 ranked 17^(th) in the keyword pool 501 is b1in FIG. 6. In addition, it is assumed that the difference betweenappearance frequency a0 and appearance frequency b1 is the thresholdvalue.

Referring to FIG. 6, the appearance frequency of keyword 1 KW1 on dateD2 is a1. Here, the difference between a1 and b1 has a larger value thanthe difference (i.e., the threshold value) between a0 and b1.Accordingly, the apparatus 100 may determine keyword 1 KW1 to be a dailykeyword of date D2.

To ensure the accuracy of a daily keyword, various embodiments may beused in operation S30, in addition to the method of comparing thekeyword pools of the first and second dates.

For example, the apparatus 100 may identify the appearance frequency ofeach keyword included in the keyword pool (formed in operation S30) ofthe first date during a particular date section. To this end, theapparatus 100 may determine a first time window based on the first date.That is, the apparatus 100 may compare the keyword pool formed based onthe text content collected on the first date with a keyword pool formedbased on text content collected during the first time window. Here, thefirst time window may be a date section consisting of a plurality ofdates including the second date.

FIG. 7 illustrates a first time window for determining a daily keyword,which is referred to in some embodiments. In FIG. 7, it is assumed thatthe first date is t1.

Referring to FIG. 7, the apparatus 100 may determine a section includingdates before t1 on a graph 710 to be a first time window 711.Alternatively, as shown on a graph 720, the apparatus 100 may determinea date section including t1 to be a first time window 721. The size ofthe first time window 711 or 721 may be determined by a user ormanufacturer of the apparatus 100.

Referring to the graph 710, the apparatus 100 may measure the dailyappearance frequency of keyword 1 KW1 included in keyword pools duringthe first time window 711. That is, the apparatus 100 may measure thefrequency of a keyword which appears repeatedly by comparing keywordpools of dates within the first time window 711. When the difference inthe appearance frequency of keyword 1 KW1 between t1 and t2 is equal toor greater than a threshold value, the apparatus 100 may determinekeyword 1 KW1 to be a daily keyword of t1. On the other hand, when thedifference in the appearance frequency of keyword 1 KW1 between t0 andt2 is less than the threshold value, the apparatus 100 may not determinekeyword 1 KW1 to be a daily keyword of t2.

Referring to the graph 720, the apparatus 100 may measure the dailyappearance frequency of keyword 1 KW1 during the first time window 721.The apparatus 100 may compare appearance frequencies of keyword 1 KW1 att2 and t3 with the appearance frequency of keyword 1 KW1 at t1. When thedifference in the appearance frequency of keyword 1 KW1 between t1 andeach of t2 and t3 is equal to or greater than a threshold value, theapparatus 100 may determine keyword 1 KW1 to be a daily keyword of t1.

According to an embodiment, the apparatus 100 may determine a dailykeyword without considering the appearance frequency of keyword 1 KW1.

For example, the keyword pool of the first date may include a newkeyword. Here, the new keyword may be a keyword which is not included ina daily keyword pool of at least one other date included in the firsttime window. Alternatively, the new keyword may be a keyword which wasposted a very small number of times during the first time window.

The apparatus 100 may determine whether a new keyword exists in thekeyword pool of the first date. In addition, the apparatus 100 maydetermine whether the new keyword was posted a predetermined number oftimes or more on the first date.

When determining that the new keyword was posted the predeterminednumber of times or more, the apparatus 100 may determine the new keywordto be a daily keyword of the first date. For the new keyword which hasnot been posted on other dates, it is not necessary for the apparatus100 to compare the keyword pool of the first date with keyword pools ofother dates.

The apparatus 100 may generate a daily keyword of the first date byperforming the above process on each keyword included in the keywordpool of the first date. That is, the first date may have one or moredaily keywords.

FIG. 8 illustrates a second time window for determining a daily keyword,which is referred to in some embodiments. In FIG. 8, it is assumed thatthe first date is t1.

To determine the appearance frequency of keyword KW1 based on the firstdate, the apparatus 100 may determine not only the first time window butalso a second time window. The second time window may be a date sectionincluding more dates than the first time window.

The apparatus 100 may determine date sections before t1 to be the firstand second time windows. On a graph 810, a first time window 711 is adate section from t1 to t0, and a second time window 811 is a datesection from t1 to t4.

Alternatively, the apparatus 100 may determine date sections includingt1 to be the first and second time windows. On a graph 820, a first timewindow 721 is a date section between t2 and t3 which includes t1, and asecond time window 821 is a date section between t5 and t6 whichincludes t1.

The apparatus 100 may identify the number of times that keyword 1 KW1was posted in text content collected during the second time window 811or 821 and the number of times that keyword 1 KW1 was posted in textcontent collected during the first time window 711 or 721. In addition,the apparatus 100 may calculate a ratio of the numbers of times thatkeyword 1 KW1 was posted and determine whether to remove each keywordincluded in a keyword pool of the first date based on the calculationresult.

When the ratio of the number of times that keyword 1 KW1 was postedduring the second time window and the number of times that keyword 1 KW1was posted during the first time window is equal to or greater than athreshold value, the apparatus 100 may leave keyword 1 KW1 in thekeyword pool. When the ratio is less than the threshold value, theapparatus 100 may remove keyword 1 KW1 from the keyword pool.

It is assumed that the threshold value is 0.7. In an example, keyword 1KW1 may be posted 100 times during the second time window and 80 timesduring the first time window. In this case, the ratio of the numbers oftimes that keyword 1 KW1 was posted during the first and second timewindows is 0.8. Since the ratio of 0.8 is greater than 0.7, theapparatus 100 may leave keyword 1 KW1 in the keyword pool. It can beunderstood here that keyword 1 KW1 was posted intensively during thefirst time window.

In another example, keyword 1 KW1 may be posted 100 times during thesecond time window and 20 times during the first time window. In thiscase, a ratio of the numbers of times that keyword 1 KW1 was posted is0.2. Since the ratio of 0.2 is less than 0.7, the apparatus 100 mayremove keyword 1 KW1 from the keyword pool. It can be understood herethat keyword 1 KW1 was posted more in other time sections than in thefirst time window.

A case where the first time window and the second time window are datesections has mainly been described above. According to an embodiment,the first time window and the second time window may be time sections,not date sections. In this case, it is assumed that the apparatus 100forms a keyword pool based on text content collected until a preset timeof the first date in operations S10 and S20.

For example, when a user terminal 200 accesses the service server 100 at2 p.m. in the system of FIG. 1, the apparatus 100 (i.e., the serviceserver 100) may generate one or more daily keywords based on the time ofaccess. Here, the apparatus 100 may determine the first time window tobe a time section until 2 hours before the access time of the firstdate. In this case, the apparatus 100 may compare a keyword pool formedbased on text content collected until 2 hours before the access timewith a keyword pool formed based on the access time.

The size of each of the first time window and the second time window maybe determined by the user or manufacturer of the apparatus 100.Alternatively, the size of each of the first time window and the secondtime window may be adjusted according to the user setting of the userterminal 200 which receives services according to embodiments from theapparatus 100. Accordingly, daily keywords of the first date may varyaccording to the user setting of the user terminal 200. To this end, theapparatus 100 may provide the user terminal 200 with a user interfacefor adjusting the size of each of the first time window and the secondtime window.

FIG. 9 illustrates a keyword refinement process using the first timewindow, and FIG. 10 illustrates a keyword refinement process using thefirst time window and the second time window. The effects of the firsttime window and the second time window will now be described in detailwith reference to FIGS. 9 and 10. In FIGS. 9 and 10, it is assumed thatthe first time window is a date section including the first date.

Referring to FIG. 9, when the first date is date D7, the apparatus 100may form a keyword pool 901 of the first date. The keyword pool 901 mayinclude keywords such as “FTA in effect,” “professional baseball,” and“new semiconductor technology” in order of appearance frequency. Inaddition, the keyword pool 901 may include “substitute holiday” with lowappearance frequency as a keyword.

A keyword pool 902 of date D8 may include keywords such as “professionalbaseball” and “substitute holiday” in order of appearance frequency. Inaddition, the keyword pool 902 may include “FTA in effect” with lowappearance frequency as a keyword.

The apparatus 100 may compare the keyword pool 902 of date D8 and thekeyword pool 901 of date D7 included in the first time window. Referringto FIG. 9, the appearance frequency of the keyword “FTA in effect” isvery high on date D7 but very low on date D8. In this case, theapparatus 100 may leave the keyword “FTA in effect” in the keyword pool901 of date D7. In addition, when the difference between the appearancefrequencies of the keyword “FTA in effect” on date D7 and date D8 isequal to or greater than a threshold value, the apparatus 100 maydetermine the keyword “FTA in effect” to be a daily keyword 910 of dateD7. In the same way, the apparatus 100 may determine the keyword “newsemiconductor technology” to be a daily keyword 910 of date D7.

On the other hand, the appearance frequency of the keyword “professionalbaseball” is not greatly different between date D7 and date D8.Therefore, the apparatus 100 may remove the keyword “professionalbaseball” from the keyword pool 901 of date D7. Accordingly, the keyword“professional baseball” may not be determined to be a daily keyword ofdate D7.

When the first date is date D8, the apparatus 100 may form the keywordpool 902 of the first date. The apparatus 100 may compare the keywordpool 901 of date D7 and the keyword pool 902 of date D8 included in thefirst time window. Referring to FIG. 9, the appearance frequency of thekeyword “substitute holiday” is very high on date D8 but very low ondate D7. In this case, the apparatus 100 may leave the keyword“substitute holiday” in the keyword pool 902 of date D8. In addition,when the difference between the appearance frequencies of the keyword“substitute holiday” on date D8 and date D7 is equal to or greater thanthe threshold value, the apparatus 100 may determine the keyword“substitute holiday” to be a daily keyword 910 of date D8. On the otherhand, the appearance frequency of the keyword “professional baseball” isnot greatly different between date D8 and date D7. Therefore, theapparatus 100 may remove the keyword “professional baseball” from thekeyword pool 902 of date D8. Accordingly, the keyword “professionalbaseball” may not be determined to be a daily keyword of date D8.

In the above example, only the keyword pool 901 of date D7 which is thefirst date and the keyword pool 902 of date D8 included in the firsttime window are compared. However, the apparatus 100 may also comparekeyword pools of a plurality of dates included in the first time windowwith the keyword pool 901 of the first date.

The apparatus 100 may determine the second time window which includesthe first time window referred to in the description of FIG. 9. In FIG.10, it is assumed that the second time window is a date sectionincluding date D2 and date D12.

When the first date is date D7, the apparatus 100 may compare aplurality of keyword pools including a keyword pool 1001 of date D2, thekeyword pool 902 of date D8 and a keyword pool 1002 of date D12 with thekeyword pool 901 of date D7.

The apparatus 100 may identify the number of times that each of thekeyword “FTA in effect” and the keyword “new semiconductor technology”was posted during the first time window including date D7 and date D8.In addition, the apparatus 100 may determine the number of times thateach of the keyword “FTA in effect” and the keyword “new semiconductortechnology” was posted during the second time window excluding the firsttime window. Referring to FIG. 10, both the keyword “FTA in effect” andthe keyword “new semiconductor technology” were posted many times in thesecond time window. The apparatus 100 may remove the keyword “FTA ineffect” and the keyword “new semiconductor technology” from the keywordpool 901 of date D7 based on ratios of the numbers of times that each ofthe keyword “FTA in effect” and the keyword “new semiconductortechnology” was posted on date D7 and other dates included in the secondtime window. Accordingly, daily keywords 1010 of date D7 may not includethe keyword “FTA in effect” and the keyword “new semiconductortechnology.”

On the other hand, the keyword “professional baseball” was posted manytimes during the first time window including date D7 and date D8 but wasposted a small number of times on other dates or was not included inkeyword pools of other dates. Therefore, the apparatus 100 may leave thekeyword “professional baseball” in the keyword pool 902 of date D8 and901 of date D7 based on ratios of the numbers of times that the keyword“professional baseball” was posted on date D8, D7, and other datesincluded in the second time section. Accordingly, the daily keywords1010 may include the keyword “professional baseball.”

When the first date is date D8, similar results to the results of theabove example may be obtained. That is, daily keywords of differentdates may include the same keyword. In addition, daily keywords ofsuccessive dates may include the same keyword. For example, when aparticular issue affects the society at large for a considerable periodof time, the apparatus 100 may extract the same keyword on differentdates using the second time window.

Referring to FIGS. 9 and 10, even when the same keyword pool 901 or 902is formed for the same date D7 or D8, the apparatus 100 can generatedifferent daily keywords 910 and 1010 using the second time window.

FIG. 11 illustrates priority rankings of daily keywords according tosource, which are referred to in some embodiments. The apparatus 100 mayidentify sources of pieces of text content collected on the first date.The apparatus 100 may identify sources of pieces of text contentincluding daily keywords of the first date.

Accordingly, the apparatus 100 may prioritize the daily keywords of thefirst date based on the identified sources. The apparatus 100 mayprioritize the daily keywords based on attributes of the identifiedsources. The attributes of a source may be determined according to thenature of the source, the media type of the source, the channel type ofthe source, etc. For example, the nature of the source may beinformation about whether the source is a public institution, a privateinstitution, or an individual. The media type of the source may beinformation about whether the source is an economic media, a sportsmedia, etc. In addition, the channel type of the source may beinformation about whether the source is Internet news, a blog, an SNS,etc. The attributes of the source may also be determined based on textcontent identified using the above-described keyword extractionalgorithm.

The apparatus 100 may identify different sections of the same source asdifferent sources. For example, the apparatus 100 may identify anentertainment news section and a political news section of Internet newsprovided by newspaper company A as different sources.

The apparatus 100 may give a different weight to a keyword according toattributes. Referring to FIG. 11, the apparatus 100 may store dailykeyword information 1100 including sources of daily keywords generatedin operation S30 and weight information according to the attributes ofthe sources. Based on the daily keyword information 1100, the apparatus100 may generate keyword priority information 1110.

Referring to the keyword information 1100, for example, weight A may be1, weight B may be 0.5, and weight C may be 0.3. In this case, keyword 2KW2 may have a priority score of (25×1)+(20×0.5)=35, and keyword 1 KW1may have the number of times (i.e., 34) that it was posted as a priorityscore. In addition, keyword 3 KW3 may have a priority score of(50×0.3)=1.5

Accordingly, the apparatus 100 may generate the keyword priorityinformation 1110. Referring to the keyword priority information 1110,keyword 2 KW2 having a highest priority score may be ranked highest bythe apparatus 100.

Even the same keyword obtained from different sources may have differentinfluences on the price of an asset. Accordingly, the apparatus 100 mayneed to identify the same keyword obtained from different sources asdifferent keywords.

According to an embodiment, when one of the daily keywords of the firstdate has different sources, the apparatus 100 may recognize the dailykeyword as different keywords. Referring to FIG. 11, the apparatus 100may identify that keyword 2 KW2 has different sources of ‘KASAN Daily’and ‘KASAN Sports.’ In addition, since the media types of the sourcesare different, the apparatus 100 may determine that the attributes ofthe sources are different. In the above example, the apparatus 100 maydetermine keyword 2 KW2 of ‘KASAN Daily’ and keyword 2 KW2 of ‘KASANSports’ to be different keywords according to the attributes of thedifferent sources.

Therefore, even if homophones are generated as daily keywords of thefirst date, the apparatus 100 can identify the homophones as differentkeywords.

In some embodiments, an asset to be matched with each keyword may belimited to an asset matched in advance with the source of each keyword.For example, an entertainment related source (e.g., an entertainmentsection of an Internet newspaper) may be matched in advance with anentertainment related stock. Therefore, it may only be determinedwhether there is a correlation between a keyword obtained from anentertainment related source and the entertainment related stock inorder to match the keyword with the entertainment related stock.

FIG. 12 illustrates assets matched with keywords which are referred toin some embodiments. As described above, the apparatus 100 may identifysources of text content including daily keywords of the first date. Inaddition, the apparatus 100 may match the daily keywords withcorresponding assets according to the sources of the text content.

In FIG. 12, keyword 1 KW1 (1201), keyword 2 KW2 (1202) and keyword 3 KW3(1203) are illustrated as examples of the daily keywords of the firstdate.

The source of text content including keyword 1 KW1 (1201) may be a blogwhich forecasts IT stock prices. In this case, the apparatus 100 maymatch keyword 1 KW1 (1201) with an asset 1210. The asset 1210 may be astock of an individual company or a group of stocks included in aparticular category.

The source of text content including keyword 2 KW2 (1202) may be anInternet magazine which posts articles about test drives. In this case,the apparatus 100 may match keyword 2 KW2 with an asset 1220. The asset1220 may be a stock of an automobile company. In addition, the source oftext content including keyword 3 KW3 may be a real estate relatedInternet community. In this case, the apparatus 100 may match keyword 3KW3 (1203) with an asset 1230. The asset 1230 may be ownership rights ofan apartment to be reconstructed in a specific area.

Until now, embodiments related to the method of automatically generatinga daily keyword using text content which is performed by the apparatus100 for automatically generating a daily keyword have been described.Hereinafter, embodiments related to methods of using the generated dailykeyword will be described.

Method of Evaluating the Relevance of a Keyword to an Asset Price

To identify the influence of each daily keyword generated in theabove-described embodiment on the price of an asset, it should bedetermined which asset corresponds to each daily keyword. Then, itshould be analyzed how each daily keyword affects a corresponding asset.A method of determining an asset corresponding to each keyword andanalyzing the influence of each keyword on a corresponding asset willbecome apparent from embodiments described below.

According to an embodiment, the apparatus 100 for evaluating therelevance of a keyword to an asset price may perform the method ofevaluating the relevance of a keyword to an asset price. The method ofevaluating the relevance of a keyword to an asset price which isperformed by the apparatus 100 for evaluating the relevance of a keywordto an asset price will now be described in detail with reference toFIGS. 13 through 20.

FIG. 13 is a flowchart illustrating a method of evaluating the relevanceof a keyword to an asset price according to an embodiment. In addition,FIG. 14 illustrates an example process of determining an assetcorresponding to a keyword, which is referred to in some embodiments.

Referring to FIG. 13, the apparatus 100 may collect text content postedon a first date through the Internet (operation S1301). The apparatus100 may generate one or more daily keywords of the first date byextracting a keyword from each piece of the text content (operationS1302). As a specific method of generating the daily keywords of thefirst date, the apparatus 100 may use the above-described method ofautomatically generating a daily keyword using text content.

The apparatus 100 may generate daily appearance frequency information ofeach daily keyword of the first date (operation S1303). The appearancefrequency information may be, for example, information expressed as ahistogram of the daily appearance frequency of each generated dailykeyword. In FIG. 14, a graph 1400 is illustrated as an example of theappearance frequency information. The appearance frequency informationmay include daily appearance frequency information in a preset datesection. Referring to FIG. 14, keyword 1 KW1 has an appearance frequencyof N1 on date t1 and an appearance frequency of N2 on date t2. Inaddition, keyword 1 KW1 has an appearance frequency of N11 on date t11located between date t1 and date t2.

The apparatus 100 may compare the daily appearance frequency informationof each generated daily keyword with daily price information of eachpre-registered asset (operation S1304). The apparatus 100 may receiveinformation about assets and hourly and daily price information of theassets from the external device 300 of FIG. 1. The apparatus 100 mayregister the received information in the storage unit 104. In FIG. 14,daily price information of asset 1 ASSET1, daily price information ofasset 2 ASSET2 and daily price information of asset 3 ASSET3 arerespectively illustrated on graphs 1401, 1402 and 1403 as examples ofthe daily price information of the pre-registered assets.

Referring to the graph 1401, asset 1 ASSET1 has a price of P0 on date t1and a price of P1 on date t2. Referring to the graph 1402, asset 2ASSET2 has a price of P1 on date t1 and a price of P2 on date t2. Inaddition, asset 2 ASSET2 has a price of P0 on date t11 located betweendate t1 and date t2. Referring to the graph 1403, asset 3 ASSET3 has aprice of P1 on date t1 and a price of P2 on date t2. In addition, asset3 ASSET3 has a price of P01 on date t11 between date t1 and date t2.

The apparatus 100 may determine an asset corresponding to each dailykeyword by comparing the daily appearance frequency information of eachdaily keyword and the daily price information of each pre-registeredasset (operation S1305). That is, the apparatus 100 may determine whichof the pre-registered assets corresponds to a particular keyword. Here,the apparatus 100 may identify, among the pre-registered assets, anasset whose daily price change during a second period is equal to orgreater than a threshold value in response to the daily appearancefrequency of each keyword during a first period. The apparatus 100 maydetermine the identified asset to be an asset corresponding to eachkeyword.

Here, the first period is a period of time during which the appearancefrequency information of each keyword is measured. The first period is apreset date section. The second period is a period of time during whicheach keyword affects a corresponding asset. The second period may be asection that begins after a certain period of time has elapsed from thefirst period. This is because a certain keyword may not immediatelyaffect a change in the price information of a corresponding asset. Forexample, if keyword A which affects asset A is generated as a dailykeyword, the price of asset A may be changed two days later.Alternatively, the second period may be a section including the firstperiod. If a certain keyword immediately affects a change in the priceinformation of a corresponding asset, the second period may be the sameperiod as the first period. The length or starting point of the secondperiod based on the first period may be set by the user or manufacturerof the apparatus 100.

The daily appearance frequency information of keyword 1 KW1 is comparedwith the daily price information of asset 1 ASSET1.

Referring to the graph 1400, the appearance frequency of keyword 1 KW1increases from N0 to N1 during the first period extending from apredetermined initial date to t1. Referring to the graph 1401, the priceof asset 1 ASSET1 falls continuously from P0 during the second periodwhich lasts a predetermined date section after t1. Here, asset 1 ASSET1may be an asset whose price is reduced by the influence of keyword 1KW1. Referring back to the graph 1400, the appearance frequency ofkeyword 1 KW1 decreases from N1 to N11 during a date section extendingfrom t1 to tn. During this date section, the price of asset 1 ASSET1falls continuously. The apparatus 100 may detect that the price of asset1 ASSET1 falls continuously while the appearance frequency of keyword 1KW1 increases or decreases. Accordingly, the apparatus 100 may determinethat asset 1 ASSET1 is not affected by keyword 1 KW1.

The daily appearance frequency information of keyword 1 KW1 is comparedwith the daily price information of each of asset 2 ASSET2 and asset 3ASSET3.

Referring to the graphs 1400, 1402 and 1403, the trend of the dailyappearance frequency of keyword 1 KW1 matches the trend of the dailyprice change of each of asset 2 ASSET2 and asset 3 ASSET3. Accordingly,the apparatus 100 may determine that the daily price change of each ofasset 2 ASSET2 and asset 3 ASSET3 corresponds to the daily appearancefrequency of keyword 1 KW1. Here, the apparatus 100 may determine asset3 ASSET3 whose price change is equal to or greater than a thresholdvalue to be an asset corresponding to keyword 1 KW1 among asset 2 ASSET2and asset 3 ASSET3. Since the price change of an asset can be affectedby factors other than a keyword, the apparatus 100 may determine thatasset 2 ASSET2 whose price change is less than the threshold value is anasset not affected by keyword 1 KW1.

Referring to the graph 1403, the price change of asset 3 ASSET3 shows asimilar pattern to the appearance frequency of keyword 1 KW1. That is,when the appearance of keyword 1 KW1 increases, the price of asset 3ASSET3 also increases. However, there may also be an asset whose pricedecreases as the appearance frequency of keyword 1 KW1 increases.

FIG. 15 illustrates another example process of determining an assetcorresponding to a keyword, which is referred to in some embodiments.Referring to FIG. 15, the apparatus 100 may identify asset 3 ASSET3 andasset 4 ASSET4 whose price changes correspond to the appearancefrequency of keyword 1 KW1. Here, a price change includes an absolutevalue of the price change. That is, referring to graphs 1400 and 1501,while the appearance frequency of keyword 1 KW1 has a positive value,the price change of asset 4 ASSET4 has a negative value. Even in thiscase, the apparatus 100 may determine asset 4 ASSET4 to be an assetcorresponding to keyword 1 KW1.

There may be a plurality of keywords corresponding to one asset. Amethod of determining a keyword having a high influence on acorresponding asset among a plurality of keywords will now be describedwith reference to FIG. 16.

FIG. 16 illustrates the influence of keywords on an asset, which isreferred to in some embodiments. Here, it is assumed that a first dateis t1 and a second date is t2. The second date t2 is a date after thefirst date t1.

The apparatus 100 may determine keyword 1 KW1 to be a daily keyword ofthe first date t1 in operation S1305. In addition, referring to FIG. 16,the apparatus 100 may compare appearance frequency information 1400 ofkeyword 1 KW1 with price information of each pre-registered asset anddetermine asset 5 ASSET5 to be an asset corresponding to the dailykeyword (keyword 1 KW1) based on the comparison result. Specifically,the apparatus 100 may identify asset 5 ASSET5 whose price change (fromP0 to P1) during a second period is equal to or greater than a thresholdvalue in response to an increase in the appearance frequency of keyword1 KW1 from N0 to N1 during a first period. In FIG. 16, each of the firstperiod and the second period extends from a predetermined initial dateto t1.

Then, the apparatus 100 may generate one or more daily keywords of thesecond date t2. The apparatus 100 may determine whether the same keywordas any one of the daily keywords of the first date t1 is included in thedaily keywords of the second date t2. For example, when keyword 1 KW1 ofthe first date t1 is “interest rate rise,” the apparatus 100 maydetermine whether “interest rate rise” is also included in the dailykeywords of the second date t2.

If the same keyword as any one of the daily keywords of the first datet1 is included in the daily keywords of the second date t2, theapparatus 100 may monitor daily price information of an asset determinedto correspond to the keyword. Here, the apparatus 100 may monitor thedaily price information of the asset for a period of time preset basedon the second date t2. In FIG. 16, the preset period of time is from t1to t2.

In the above example, the apparatus 100 may monitor daily priceinformation of asset 5 ASSET5 corresponding to “interest rate rise.” InFIG. 16, a graph 1601 is illustrated as an example of the daily priceinformation of asset 5 ASSET5 corresponding to “interest rate rise.” Theapparatus 100 may monitor that the daily price of asset 5 ASSET5 changesfrom P1 to P2 as shown on the graph 1601 when the appearance frequencyof “interest rate rise” changes from N1 to N2 as shown on the graph1400.

Based on the monitoring result, the apparatus 100 may determinerelevance information of the keyword “interest rate rise” to asset 5ASSET5. Here, the relevance information may include information aboutwhether the keyword “interest rate rise” has an influence on the pricechange of asset 5 ASSET5 and information about an influence index of thekeyword “interest rate rise” on the price of asset 5 ASSET5.

The apparatus 100 may measure a ratio of the appearance frequency of thedaily keyword (“interest rate rise”) of the first date t1 and the pricechange of asset 5 ASSET5 and a ratio of the appearance frequency of thedaily keyword (“interest rate rise”) of the second date t2 and the pricechange of asset 5 ASSET5. Based on the measured ratios, the apparatus100 may identify the influence of keyword 1 KW1 on asset 5 ASSET5.

In addition, when monitoring that the price change of asset 5 ASSET5from t1 to t2 is equal to or greater than the threshold value, theapparatus 100 may update the relevance information of the keyword“interest rate rise” to asset 5 ASSET5. In the above example, when thedifference between P1 and P2 on the graph 1601 is equal to or greaterthan the threshold value, the apparatus 100 may upgrade the influenceindex of the keyword “interest rate rise” on asset 5 ASSET5. This isbecause the relevance of the keyword “interest rate rise” to asset 5ASSET5 which had been determined based on the first date t1 wasreconfirmed based on the second date t2.

Similarly, when keyword 2 KW2 is determined to be a daily keyword of thefirst date t1, the apparatus 100 may determine asset 5 ASSET5 as anasset corresponding to keyword 2 KW2. It is assumed that keyword 2 KW2is “inflation.” The apparatus 100 may generate one or more dailykeywords of the second date t2 and, if “inflation” is also included inthe daily keywords of the second date t2, may identify the keyword“inflation” among the daily keywords of the second date t2

Then, the apparatus 100 may monitor the daily price information of asset5 ASSET5 corresponding to the daily keyword (keyword 2 KW2) of the firstdate t1. In FIG. 16, a graph 1602 is illustrated as an example of thedaily price information of asset 5 ASSET5 corresponding to “inflation.”The apparatus 100 may monitor that the daily price of asset 5 ASSET5changes from P1 to P2 as shown on the graph 1602 when the appearancefrequency of “inflation” changes from N1 to N2 as shown on a graph 1600.Accordingly, the apparatus 100 may identify the influence of keyword 2KW2 on asset 5 ASSET5.

Based on the identified influence, the apparatus 100 may determine whichof keyword 1 KW1 and keyword 2 KW2 has priority for asset 5 ASSET5.

The influence of each keyword on an asset ultimately denotes theinfluence of each daily keyword on the price of a corresponding asset.That is, the apparatus 100 may determine how much the price of an assetis increased or decreased by the influence of a keyword. The apparatus100 may store the determination result as relevance information.

Then, the apparatus 100 may predict changes in the price of the asset onother dates based on the stored relevance information.

As described above, the apparatus 100 may determine the relevanceinformation of each daily keyword to an asset. Various indices of therelevance information will now be described.

FIG. 17 illustrates the difference between a time when a keyword isgenerated and a time when the price of an asset is changed, which isreferred to in some embodiments. FIG. 18 illustrates a period of timeduring which a keyword affects an asset, which is referred to in someembodiments.

In FIG. 17, it is assumed that keyword 1 KW1 is determined to be a dailykeyword of the first date t1 and a daily keyword of the second date t2as shown on a graph 1400. In addition, it is assumed that assetscorresponding to keyword 1 KW1 are asset 6 ASSET6 and asset 7 ASSET7.

Referring to FIG. 17, while the appearance frequency of keyword 1 KW1increased during a first period (from a predetermined initial date tot1), the price of asset 6 ASSET6 also increased during a second period(from a predetermined initial date to t01) as shown on a graph 1701.

The apparatus 100 may determine which of the first period and the secondperiod precedes the other one. Then, the apparatus 100 may store thedetermination result as relevance information of keyword 1 KW1 (dailykeyword of the first date t1) to asset 6 ASSET6. Referring to the graph1701, the price of asset 6 ASSET6 changed before the generation date(the first date t1) of keyword 1 KW1.

Therefore, when keyword 1 KW1 is determined to be a daily keyword of thesecond date t2, the apparatus 100 may predict that the price of asset 6ASSET6 will change before the second date t2 based on the storedrelevance information.

On a graph 1702, the price of asset 7 ASSET7 increased during a secondperiod (from a predetermined initial date to t11). In this case, theapparatus 100 may also determine which of the first period and thesecond period precedes the other one and store the determination resultas relevance information of asset 7 ASSET7. Referring to the graph 1702,the price of asset 7 ASSET7 changed after the generation of keyword KW1.

Therefore, when keyword KW1 is determined to be a daily keyword of thesecond date t2, the apparatus 100 may predict that the price of asset 7ASSET7 will change after the second date t2 based on the storedrelevance information.

In addition, the apparatus 100 may measure a time gap between the firstperiod and the second period. Then, the apparatus 100 may store themeasurement result as the relevance information of keyword 1 KW1 toasset 7 ASSET7. Referring to the graph 1702, although keyword 1 KW1 wasgenerated as a daily keyword on the first date t1, the price of asset 7ASSET7 affected by keyword 1 KW1 changed on t11. Accordingly, theapparatus 100 may determine that keyword 1 KW1 begins to affect asset 7ASSET7 after the time gap (t11-t1).

Therefore, when keyword 1 KW1 is determined to be a daily keyword of thesecond date t2, the apparatus 100 may predict that the price of asset 7ASSET7 will change after a time gap (t21-t2) based on the storedrelevance information.

In FIG. 18, it is assumed that keyword 1 KW1 is determined to be a dailykeyword of the first date t1 and a daily keyword of the second date t2as shown on a graph 1400.

Referring to FIG. 18, while the appearance frequency of keyword 1 KW1increased during a first period (from a predetermined initial date tot1), the price of an asset increased as shown on a graph 1800. Inaddition, the increased price was maintained during a second period E1.

The apparatus 100 may store the second period E1 during which theinfluence of keyword 1 KW1 on the price of the asset was maintained asrelevance information.

Therefore, when keyword 1 KW1 is determined to be a daily keyword of thesecond date t2, the apparatus 100 may predict that the price of theasset will be maintained during the second period E2 based on the storedrelevance information.

A plurality of daily keywords may be generated for the first date inoperation S1302. If the price of a specific asset increases on the firstdate according to the appearance frequency information of the dailykeywords, all of the daily keywords may affect the price of the asset.Alternatively, any one of the daily keywords may not affect the price ofthe asset. This will now be described with reference to FIGS. 19 and 20.

FIG. 19 illustrates a process of identifying a keyword that affects anasset among a plurality of keywords, which is referred to in someembodiments.

Referring to FIG. 19, it is assumed that keyword 1 KW1 and keyword 2 KW2of a graph 1900 are included in daily keywords of the first date t1. Inaddition, a graph 1910 is illustrated in FIG. 19 as an example of anasset determined to correspond to keyword 1 KW1 and keyword 2 KW2.

In operation S1305, the apparatus 100 may determine that both keyword 1KW1 and keyword 2 KW2 affect the determined asset based on the firstdate t1. When keyword 1 KW1 is determined to be a daily keyword of thesecond date t2, the apparatus 100 may identify that one of the dailykeywords of the first date t1 has been determined again to be a dailykeyword of the second date t2.

Therefore, the apparatus 100 may monitor daily price information of thedetermined asset during a period of time preset based on the second datet2.

In FIG. 19, a graph 1911 is illustrated as an example of the daily priceinformation of the asset. The apparatus 100 may determine relevanceinformation of keyword 1 KW1 to the asset based on the monitoringresult. The apparatus 100 may compare the graphs 1910 and 1911 anddetermine that keyword 1 KW1 has high relevance to the asset based onthe comparison result.

On the other hand, when keyword 2 KW2 is determined to be a dailykeyword of the second date t2, the apparatus 100 may identify that oneof the daily keywords of the first date t1 has been determined again tobe a daily keyword of the second date t2. Therefore, the apparatus 100may monitor the daily price information of the determined asset during aperiod of time preset based on the second date t2.

In FIG. 19, a graph 1912 is illustrated as an example of the daily priceinformation of the asset. The apparatus 100 may determine relevanceinformation of keyword 2 KW2 to the asset based on the monitoringresult. The apparatus 100 may compare the graphs 1910 and 1912 anddetermine that keyword 2 KW2 has no relevance to the asset based on thecomparison result. In this case, the apparatus 100 may modify itsdetermination that both keyword 1 KW1 and keyword 2 KW2 affect the assetbased on the first date t1. That is, since keyword 2 KW2 is irrelevantto the asset, the apparatus 100 may modify the daily keywords registeredon the first date t1. Here, the apparatus 100 may also determine thatthere was an error in keyword generation on the first date t1 and adjustthe size of each of the first window and the second window describedabove in the embodiment of the method of automatically generating adaily keyword using text content.

FIG. 20 illustrates an asset affected by a plurality of keywords, whichis referred to in some embodiments. A repetitive description of featuresdescribed above with reference to FIG. 19 will be omitted.

In operation S1305, the apparatus 100 may determine that both keyword 1KW1 and keyword 2 KW2 affect an asset based on the first date t1. Then,the apparatus 100 may generate one or more daily keywords of the seconddate t2. If keyword 1 KW1 and keyword 2 KW2 are also included in thegenerated daily keywords of the second date t2, the apparatus 100 maymonitor daily price information of the asset determined to correspond tothe keywords (keyword 1 KW1 and keyword 2 KW2).

The apparatus 100 may determine relevance information of the keywords tothe asset based on the monitoring result.

For example, it is assumed that both keyword 1 KW1 and keyword 2 KW2which are daily keywords of the first date t1 affect the asset as shownon a graph 1910.

The apparatus 100 may generate keyword 1 KW1 as a daily keyword of thesecond date t2 as shown on a graph 1901. If the daily keyword (keyword 1KW1) of the second date t2 does not affect the asset as shown on a graph2001, the apparatus 100 may determine that keyword 1 KW1 is irrelevantto the asset.

If keyword 2 KW2 does not affect the asset as shown on a graph 2002, theapparatus 100 may also determine that keyword 2 KW2 is irrelevant to theasset.

It is assumed that the price of the asset increases to a threshold valueor more when both keyword 1 KW1 and keyword 2 KW2 are included in dailykeywords of another date as in the daily keywords of the first date t1.

Based on the price information of the asset on the first date t1, thesecond date t2 and another date, the apparatus 100 may determine thatthe asset is affected by a plurality of keywords (both keyword 1 KW1 andkeyword 2 KW2) and not by an individual keyword.

Therefore, the apparatus 100 may store a pair of keyword 1 KW1 andkeyword 2 KW2 as relevance information to the asset.

Specific Embodiment of an Apparatus for Evaluating the Relevance of aKeyword to an Asset Price

According to the above-described embodiments, the apparatus 100 maydetermine an asset corresponding to a daily keyword and analyze theinfluence of the daily keyword on the asset. In particular, based onrelevance information of a daily keyword of a first date to acorresponding asset, the apparatus 100 may predict the influence of thedaily keyword on the price of the asset on a second date. Then, theapparatus 100 may provide a user terminal 200 with an investmentguidance service on the asset based on its prediction.

To provide the investment guidance service, the apparatus 100 may storeinformation about daily keywords and assets corresponding to the dailykeywords in the storage unit 104. In addition, the apparatus 100 maystore the result of analyzing the influence of each daily keyword on acorresponding asset. For example, the apparatus 100 may store relevanceinformation described above with reference to FIGS. 16 through 18.

FIG. 21 illustrates relevance information of keywords to assets, whichis referred to in some embodiments. In addition, FIG. 22 illustratesrelevance indices of keywords to assets, which are referred to in someembodiments.

In FIG. 21, data about relevance information CR of each daily keyword KWto a corresponding asset A is illustrated. The data may be stored in thestorage unit 104 of the apparatus 100. Referring to FIG. 21, the datamay include daily keyword information of each date and asset informationcorresponding to the daily keyword information. In this case, theapparatus 100 may store the relevance information CR based on thegeneration date of each daily keyword KW. Alternatively, the apparatus100 may store the relevance information CR of a corresponding dailykeyword KW based on the type of each asset A. In addition, the data mayinclude priority information based on sources of the daily keywords KW.

Each piece of the relevance information CR of FIG. 21 may includeinformation about relevance indices. The relevance indices areinformation about the specific influence of each daily keyword on acorresponding asset.

Referring to FIG. 22, the relevance information CR includes informationabout the following relevance indices.

The relevance information CR may include information about the influenceof a daily keyword on the price of an asset. That is, the relevanceinformation CR is information about whether the price of an assetincreases or decreases in response to a specific daily keywordgenerated.

The relevance information CR may include information about a time gapbetween a time when a daily keyword is generated and a time when theprice of an asset is changed by the influence of the daily keyword. Thatis, the relevance information CR is information about how much timeafter the generation of a daily keyword the price of an asset is changedby the influence of the daily keyword.

The relevance information CR may include information about a period oftime during which a daily keyword has an influence on the price changeof an asset. That is, the relevance information CR is information abouta period of time during which the price of an asset fluctuatescontinuously in response to a daily keyword generated.

The relevance information CR may include influence information of adaily keyword on the price of an asset. That is, the relevanceinformation CR is information about how much the price of an asset isincreased or decreased by a daily keyword generated.

The relevance information CR may include reliability information of therelevance indices. That is, the relevance information CR is informationabout the accuracy of predicting an asset price based on the relevanceindices. For example, when there is relevance information stored for adaily keyword generated on a first date, if the same keyword as thedaily keyword is generated on a second date after the first date, theapparatus 100 may determine whether the price of an asset is changedaccording to the relevance information of the first date. Then, theapparatus 100 may store the determination result as a relevance index inthe relevance information CR of FIG. 22.

Hereinafter, the investment guidance service provided by the apparatus100 to a user terminal 200 using the relevance information will bedescribed with reference to FIGS. 23 through 26.

FIG. 23 illustrates an example graphic user interface (GUI) forproviding daily keywords, according to an embodiment.

Referring to FIG. 23, the apparatus 100 may provide a GUI 2300 for theinvestment guidance service to a user terminal 200. The GUI 2300 mayinclude daily keyword information 2301 of a first date. In FIG. 23, theGUI 2300 displays daily keywords generated based on text contentcollected on the first date as an example of the daily keywordinformation 2301 of the first date.

When any one 2302 of the daily keywords is selected through the userterminal 200, the apparatus 100 may generate an interface 2310 inresponse to the selection of the keyword 2302. In addition, theapparatus 100 may provide the interface 2310 to the user terminal 200.

The interface 2310 may include asset information corresponding to theselected keyword 2302. Specifically, the interface 2310 may includeinformation about one or more assets 2311 through 2313 corresponding tothe selected keyword 2302. In addition, the interface 2310 may includean interface 2314 for selecting the asset information by type.

The apparatus 100 may provide the investment guidance service using therelevance information of each keyword to an asset and the informationabout relevance indices described above with reference to FIGS. 21 and22.

FIG. 24 illustrates an investment guidance interface based on a timewhen a keyword affects the price of an asset, which is referred to insome embodiments.

Referring to FIG. 24, when one 2302 of daily keywords is selected, theapparatus 100 may provide an interface 2400 to a user terminal 200. InFIG. 24, the interface 2400 includes stock price information 2401 of oneor more companies corresponding to the keyword 2302 as asset informationcorresponding to the keyword 2302.

When company A is selected through the user terminal 200, the apparatus100 may provide an interface 2410 to the user terminal 200. Theinterface 2410 may include information 2411 about the stock price ofcompany A which has fluctuated in response to the keyword 2302. Forexample, the stock price information 2411 may be information about achange in the stock price of company A during a preset period of time.

In addition, the interface 2410 may include relevance information of thekeyword 2302 to the stock of company A. For example, the interface 2410may include information 2412 about how the stock price of company A isaffected by the keyword 2302. The interface 2410 may also includeinformation 2314 and 2413 about a time gap after which the keyword 2302affects the stock price of company A. In addition, the interface 2410may include information 2413 about a period of time during which thekeyword 2302 affects the stock price of company A.

In addition to providing the above relevance information, the apparatus100 may transmit a message 2414 for providing investment guidance on thestock of company A to the user terminal 200.

The message 2414 may include a recommendation for the purchase or saleof the stock of company A. In addition, the message 2414 may includeguidance on the purchasing or selling timing of the stock of company Aand the holding period of the stock of company A based on the time gapinformation and the information about a period of time during which thekeyword 2302 affects the stock price of company A.

FIG. 25 illustrates an investment guidance interface based on the degreeof influence of a keyword on the price of an asset, which is referred toin some embodiments.

When a user inputs a keyword 2501 to a user terminal 200, the apparatus100 may receive the keyword 2501. The apparatus 100 may generate aninterface 2500 in response to the keyword 2501 input by the user. Theinterface 2500 may include information about assets 2502 correspondingto the input keyword 2501.

The apparatus 100 may determine whether the input keyword 2501 matchesany one of pre-stored daily keywords of the very date on which thekeyword 2501 was input or any one of pre-stored daily keywords ofanother date. That is, when a keyword corresponding to a daily keywordis input, the apparatus 100 can identify asset information correspondingto the input keyword.

When any one of the assets 2502 is selected, the apparatus 100 maygenerate an interface 2510. In addition, the apparatus 100 may transmitthe generated interface 2510 to the user terminal 200.

The interface 2510 may include information 2511 about the price of theselected asset which has fluctuated in response to the input keyword2501 and relevance information. For example, the interface 2510 mayinclude information 2512 about whether the influence of the keyword 2501precedes or follows a change in the price of the asset. The interface2510 may also include information 2513 about a time gap after which thekeyword 2501 affects the price of the asset. In addition, the interface2510 may include information about the influence of the keyword 2501 onthe price of the asset, that is, information 2514 about the influence ofthe keyword 2501 on the price change of the asset.

In addition to providing the above relevance information, the apparatus100 may transmit a message 2515 for providing investment guidance on thestock of company SA to the user terminal 200.

The message 2515 may include a recommendation about the purchase or saleof the asset. The apparatus 100 may also generate target profitinformation expected when the asset is invested based on the influenceinformation. In this case, the message 2514 may include the targetprofit information.

The investment guidance service provided by the apparatus 100 when akeyword is selected or input by a user has been described above.According to an embodiment, the apparatus 100 may provide the userterminal 200 with keyword information for an asset that the user isholding or interested in. That is, the apparatus 100 may provide akeyword for an asset that the user is holding or interested in, therebyoffering the user an opportunity to cope with a situation where thekeyword is determined to be a daily keyword.

FIG. 26 illustrates a daily keyword corresponding to an asset accordingto an embodiment. Referring to FIG. 26, the apparatus 100 may receiveinformation about the selection of an asset from a user terminal 200. Tothis end, the apparatus 100 may provide an interface 2600 to the userterminal 200. The interface 2600 may include an asset list 2601. Theasset list 2601 may include one or more assets 2602. When any one of theassets 2602 is selected by a user, the apparatus 100 may receiveinformation about user's selection and generate an interface 2610. Inaddition, the apparatus 100 may provide the interface 2610 to the userterminal 200. The interface 2610 may include information about theselected asset 2602 and a keyword list 2611 corresponding to theselected asset 2602.

The apparatus 100 may store the information about the asset 2602selected by the user. In addition, the apparatus 100 may identify akeyword corresponding to the selected asset 2602 among one or more dailykeywords of a first date. Then, the apparatus 100 may generate one ormore daily keywords of a second date. Here, if the keyword correspondingto the selected asset 2602 is included in the daily keywords of thesecond date, the apparatus 100 may recognize this fact and transmit thekeyword corresponding to the selected asset 2602 to the user terminal200. Accordingly, the user may recognize that the price of the asset2602 the user is holding or interested in can be changed. In addition,the apparatus 100 may determine that a change in the price of the asset2502 selected by the user is likely based on the fact that the keywordcorresponding to the selected asset 2602 is included in the dailykeywords of the second date. Therefore, the apparatus 100 may transmitan investment guidance message to the user terminal 200 based on thedetermination result.

FIG. 27 is a flowchart illustrating a method of extracting a dailykeyword corresponding to a price change of an asset according to anembodiment. FIG. 28 illustrates a service of, when the price of an assetis changed, recommending another asset according to an embodiment.

As described above, the influence of a keyword may not necessarilyprecede a change in the price of a corresponding asset. That is, akeyword corresponding to an asset can be determined to be a dailykeyword after the price of the asset is changed. Hereinafter, a methodof identifying a keyword corresponding to an asset after the price ofthe asset is changed will be described. In addition, a method ofidentifying another asset whose price is expected to change in responseto the identified keyword will be described.

Referring to FIG. 27, the apparatus 100 may identify an asset whosedaily price change during a first period is equal to or greater than athreshold value among pre-registered assets (operation S2701). Inaddition, the apparatus 100 may generate one or more daily keywords of afirst date by collecting text content of the first date (operationS2702). Here, operation S2702 may not be performed after operationS2701. That is, the apparatus 100 may perform operation S2702 separatelyfrom operation S2701. In addition, the first date may be a current date.That is, the apparatus 100 may extract keywords from text contentcollected every day and generate one or more daily keywords of the firstdate based on the extracted keywords.

The apparatus 100 may detect daily appearance frequency of each dailykeyword of the first date during a second period (operation S2703). Theapparatus 100 may extract a keyword whose daily appearance frequencyduring the second period corresponds to the daily price change of theasset during the first period (operation S2704). The apparatus 100 mayextract the keyword from the daily keywords of the first date.

The apparatus 100 may determine the extracted keyword to be a keywordcorresponding to the asset (operation S2705).

Next, the apparatus 100 may detect a price change of the asset which isequal to or greater than the threshold value during a third period. Inaddition, the apparatus 100 may identify another asset corresponding tothe extracted keyword among the pre-registered assets. Then, theapparatus 100 may transmit information about the identified asset to auser terminal 200. Therefore, when the price of a specific asset ischanged, the apparatus 100 may predict a change in the price of anotherasset different from the specific asset based on the change in the priceof the specific asset. In addition, the apparatus 100 may provideinvestment guidance to the user terminal 200 based on its prediction.

Referring to FIG. 28, the apparatus 100 may transmit an interface 2800which displays information about price changes of pre-registered assetsto a user terminal 200. Asset price change information 2801 may includeinformation about price fluctuations of assets during a first period.

The apparatus 100 may identify a keyword corresponding to any one of theassets based on the price change of the asset included in the assetprice change information 2801.

Referring to the asset price change information 2801 of FIG. 28, companyA experienced a stock price change of 20%, and company B experienced astock price change of 5%. For example, if a threshold value of the pricechange is 15%, the apparatus 100 may identify a keyword whose appearancefrequency corresponds to the stock of company A.

To identify the keyword, the apparatus 100 may store daily keywordinformation in advance. That is, the apparatus 100 may identify thekeyword from the pre-stored daily keyword information. Then, theapparatus 100 may determine the identified keyword to be a keywordcorresponding to the asset.

After identifying the keyword, the apparatus 100 may generate aninterface 2810 and provide the interface 2810 to the user terminal 200.The interface 2810 may include an asset 2811 whose price change is equalto or greater than the threshold value and keyword information 2812determined to correspond to the asset 2811. The keyword information 2812may include one or more keywords 2813 through 2815. The apparatus 100may prioritize the keywords 2813 through 2815 based on sources of thekeywords 2813 through 2815, and the interface 2810 may include priorityinformation of the keywords 2813 through 2815.

The apparatus 100 may identify an asset corresponding to any one of thekeywords 2813 through 2815 included in the keyword information 2812. Theidentified asset may be different from the asset 2811 whose price changeis equal to or greater than the threshold value. The apparatus 100 maygenerate an interface 2820 which includes any one 2814 of the keywords2813 through 2815 and asset information 2821 corresponding to thekeyword 2814. The apparatus 100 may transmit the interface 2820 to theuser terminal 200.

Therefore, when the price change of a specific asset is equal to orgreater than the threshold value, the apparatus 100 may provide the userterminal 200 with an investment guidance service on another asset whoseprice is expected to change.

Method of Displaying Asset Information Matched with Text Content

Embodiments of using relevance information of a keyword to acorresponding asset have been described above. The relevance of thekeyword to the asset can be extended to text content which includes thekeyword. This will now be described in detail with reference to FIG. 29.

FIG. 29 is a conceptual diagram illustrating the matching relationshipbetween text content, keywords and assets according to an embodiment. InFIG. 29, Internet news is illustrated as an example of text content. Itis assumed that keywords 2901 and corresponding assets 2903 are matchedand stored accordingly according to the above-described embodiments.

A user terminal 200 may display Internet news 2905 on a display unit.The news 2905 may include one or more keywords. In FIG. 29, the news2905 includes keyword 1 KW1, keyword 2 KW2 and keyword 3 KW3.

The user terminal 200 may detect keyword 1 KW1, keyword 2 KW2 andkeyword 3 KW3 in the news 2905 and extract keyword 1 KW1, keyword 2 KW2and keyword 3 KW3 as indicated by reference numeral 2911. Afterextracting keyword 1 KW1, keyword 2 KW2 and keyword 3 KW3, the userterminal 200 may extract assets 2913, 2923 and 2933 respectively matchedwith the extracted keyword 1 KW1, keyword 2 KW2 and keyword 3 KW3 frompre-registered assets.

In FIG. 29, of the pre-registered assets, asset 1, asset 2 and asset 3are illustrated as the assets 2913 matched with keyword 1 KW1. Inaddition, asset 1, asset 3 and asset 4 are illustrated as the assets2923 matched with keyword 2 KW2. The assets 2933 matched with keyword 3KW3 are asset 3 and asset 5.

The user terminal 200 may identify the matched assets and extract thematched assets from the pre-registered assets. In addition, when thereis an asset which has been extracted a preset number of times or more,the user terminal 200 may match the asset with the text content.

Referring again to FIG. 29, of the extracted assets, asset 3 has beenextracted three times. For example, if the preset number of times is 3times, asset 3 may be matched with the news 2905.

The user terminal 200 may display information about the asset which hasbeen extracted the preset number of times or more in a second areadifferent from a first area. That is, the information about asset 3 maybe displayed in an area different from an area where the news 2905 isdisplayed.

Assets matched with one or more keywords may include an asset whosedaily price change during a second period is equal to or greater than athreshold value in response to the daily appearance frequency of each ofthe keywords during a first period. That is, the matched assets may beassets matched with the keywords according to the above-describedembodiments. In addition, information about the asset may includeprediction information about the price of the asset which has beenextracted the preset number of times or more, wherein the predictioninformation about the price of the asset is determined based onrelevance information of at least one keyword to the asset which hasbeen extracted the preset number of times or more. That is, theinformation about the asset may include the result of predicting pricechanges of the assets according to the above-described embodiments.

If pieces of text content are matched with assets as described above, auser can retrieve relevant text content based on an asset using the userterminal 200.

To this end, the user terminal 200 may display information about anasset in the first area of the display unit.

The information about the asset may be information about the prices,price changes, etc. of the asset provided on a web page at the requestof the user.

The user terminal 200 may display a list of pieces of text contentmatched with the asset in the second area different from the first area.Here, each piece of the text content matched with the asset may includeone or more keywords matched with the asset. For example, each piece ofthe text content may be an Internet news article including the keywordsmatched with the asset. In addition, the list of the pieces of the textcontent may be a list of Internet news articles.

When any one of the pieces of the text content on the displayed list isselected, the user terminal 200 may display the selected piece of thetext content. In the above example, the user terminal 200 may display anInternet news article including the keywords.

The methods according to the embodiments described above with referenceto the accompanying drawings may be performed by the execution of acomputer program implemented as computer-readable code. The computerprogram may be transmitted from a first computing device to a secondcomputing device through a network such as the Internet and theninstalled in the second computing device for use. Each of the firstcomputing device and the second computing device may be a fixedcomputing device such as a server device or a desktop PC or a mobilecomputing device such as a notebook computer, a smartphone or a tabletPC.

According to the inventive concept, there is provided a method andapparatus for determining the influence of a keyword collected on theInternet on an asset.

According to the inventive concept, there is also provided a method andapparatus for predicting how much the price of an asset will be changedby the influence of a keyword collected on the Internet.

According to the inventive concept, there is also provided a method andapparatus for predicting a period of time during which a keywordcollected on the Internet will affect the price of an asset.

According to the inventive concept, there is also provided a method andapparatus for predicting a time when a keyword collected on the Internetwill affect the price of an asset.

According to the inventive concept, there is also provided a method andapparatus for providing investment guidance on an asset to a user bypredicting various effects of a keyword collected on the Internet on theprice of the asset.

In addition, according to the inventive concept, since a keyword thataffects the price of an asset being held or targeted by a user isprovided to the user, the user can secure the ability to respond to thekeyword.

While the inventive concept has been particularly shown and describedwith reference to exemplary embodiments thereof, it will be understoodby those of ordinary skill in the art that various changes in form anddetail may be made therein without departing from the spirit and scopeof the inventive concept as defined by the following claims. Theexemplary embodiments should be considered in a descriptive sense onlyand not for purposes of limitation.

What is claimed is:
 1. A method of automatically generating a dailykeyword using text content by a service server, the method comprising:collecting text content posted on a first date through the Internet;extracting a keyword from each piece of the text content and forming akeyword pool of the extracted keywords of the first date; and generatingone or more daily keywords of the first date using the result ofcomparing the keyword pool of the first date and a keyword pool of asecond date.
 2. The method of claim 1, wherein the generating of thedaily keywords of the first date comprises: determining a first timewindow based on the first date; comparing the keyword pool of the firstdate with a keyword pool of at least one date included in the first timewindow; and generating one or more daily keywords of the first dateusing the comparison result.
 3. The method of claim 1, wherein thegenerating of the daily keywords of the first date comprises:determining a first time window based on the first date; determiningwhether a new keyword posted more than a predetermined number of timesis included in the keywords of the keyword pool of the first date; andgenerating one or more daily keywords including the new keyword when thenew keyword is included in the keywords of the keyword pool of the firstdate, wherein the new keyword is not included in a daily keyword pool ofat least one other date within the first time window.
 4. The method ofclaim 1, wherein the generating of the daily keywords of the first datecomprises: determining a first time window and a second time windowbased on the first date; determining whether to remove each of thekeywords included in the keyword pool of the first date based on a ratioof the number of times that each of the keywords was posted within thesecond time window and the number of times that each of the keywords wasposted within the first time window; and generating one or more dailykeywords of the first date based on the determination result, whereinthe second time window comprises more dates than the first time window.5. The method of claim 1, wherein the generating of the daily keywordsof the first date comprises: identifying sources of pieces of thecollected text content which comprise the daily keywords of the firstdate; and prioritizing the daily keywords of the first date based on theidentified sources.
 6. The method of claim 5, wherein when one of thedaily keywords of the first date has different sources, the generatingof the daily keywords of the first date comprises determining thekeyword to be different keywords according to attributes of each of thedifferent sources.
 7. The method of claim 1, wherein the generating ofthe daily keywords of the first date comprises: identifying sources ofpieces of the collected text content which comprise the daily keywordsof the first date; and matching the daily keywords of the first datewith assets based on the identified sources.
 8. A method of evaluatingthe relevance of a keyword to an asset price by a service server, themethod comprising: collecting text content posted on a first datethrough the Internet; generating one or more daily keywords of the firstdate by extracting a keyword from each piece of the text content;generating daily appearance frequency information of each daily keywordof the first date; and determining an asset corresponding to each dailykeyword by comparing the generated daily appearance frequencyinformation of each daily keyword with daily price information of eachpre-registered asset.
 9. The method of claim 8, wherein the determiningof the asset corresponding to each daily keyword comprises: identifyingan asset whose daily price change during a second period is equal to orgreater than a threshold value in response to the daily appearancefrequency of each daily keyword during a first period; and determiningthe identified asset to be an asset corresponding to each daily keyword.10. The method of claim 9, wherein the price change comprises anabsolute value of the price change.
 11. The method of claim 8, whereinthe determining of the asset corresponding to each daily keywordcomprises: determining whether the same keyword as a daily keyword ofthe first date is included in one or more daily keywords of a seconddate; monitoring daily price information of an asset determined tocorrespond to the same keyword when the same keyword is included in thedaily keywords of the second date; and determining relevance informationof the same keyword to the determined asset based on the monitoringresult, wherein the daily price information of the determined asset isdaily price information of the determined asset during a preset periodof time based on the second date.
 12. The method of claim 11, whereinthe determining of the relevance information comprises updating therelevance information of the same keyword to the determined asset whenit is monitored that the price change of the determined asset during thepreset period of time is equal to or greater than a threshold value. 13.The method of claim 8, wherein the determining of the assetcorresponding to each daily keyword comprises: when a plurality of dailykeywords of the first date which correspond to the same asset exist,determining whether any one of the plurality of the daily keywords isincluded in daily keywords of the second date; monitoring daily priceinformation of the asset determined to correspond to the any one of thekeywords when the any one of the daily keywords is included in the dailykeywords of the second date; and determining relevance information ofthe any one of the daily keywords to the determined asset based on themonitoring result, wherein the daily price information of the determinedasset is daily price information of the determined asset during a periodof time within a preset range from the second date.
 14. The method ofclaim 8, wherein the determined of the asset corresponding to each dailykeyword comprises: when a plurality of daily keywords of the first datewhich correspond to the same asset exist, determining whether theplurality of daily keywords are included in daily keywords of the seconddate; monitoring daily price information of the asset determined tocorrespond to the plurality of daily keywords when the daily keywordsare included in the daily keywords of the second date; and determiningrelevance information of the plurality of daily keywords to thedetermined asset based on the monitoring result, wherein the daily priceinformation of the determined asset is daily price information of thedetermined asset during a period of time within a preset range from thesecond date.
 15. An apparatus for evaluating the relevance of a keywordto an asset price, the apparatus comprising: one or more processors; amemory which loads a computer program executed by the processors; astorage unit which stores daily price information of each pre-registeredasset and daily keywords generated by the execution of the computerprogram; and a network interface which transmits the daily keywords,wherein the computer program comprises: an operation of collecting textcontent posted on a first date through the Internet; an operation ofgenerating one or more daily keywords of the first date by extracting akeyword from each piece of the text content; an operation of generatingdaily appearance frequency information of each daily keyword of thefirst date; and an operation of determining an asset corresponding toeach daily keyword by comparing the generated daily appearance frequencyinformation of each daily keyword with the daily price information ofeach pre-registered asset.
 16. The apparatus of claim 15, wherein theoperation of determining the asset corresponding to each daily keywordcomprises: an operation of identifying an asset whose daily price changeduring a second period is equal to or greater than a threshold value inresponse to the daily appearance frequency of each daily keyword duringa first period; and an operation of determining the identified asset tobe an asset corresponding to each daily keyword.
 17. The apparatus ofclaim 16, wherein the computer program further comprises: an operationof generating one or more daily keywords of a second date; and anoperation of determining whether the same keyword as any one of thedaily keywords of the first date is included in the daily keywords ofthe second date.
 18. The apparatus of claim 17, wherein the operation ofdetermining the asset corresponding to each daily keyword comprises anoperation of measuring a time gap between the first period and thesecond period and an operation of storing the result of measuring thetime gap as relevance information of each daily keyword to thecorresponding asset, wherein the computer program further comprises anoperation of transmitting investment guidance on the corresponding assetto a user terminal based on the relevance information when it isdetermined that the same keyword as the any one of the daily keywords ofthe first date is included in the daily keywords of the second date. 19.The apparatus of claim 17, wherein the operation of determining theasset corresponding to each daily keyword comprises an operation ofdetermining which of the first period and the second period precedes theother period and an operation of storing the determination result asrelevance information of each daily keyword to the corresponding asset,wherein the computer program further comprises an operation oftransmitting investment guidance on the corresponding asset to a userterminal based on the relevance information when it is determined thatthe same keyword as the any one of the daily keywords of the first dateis included in the daily keywords of the second date.
 20. The apparatusof claim 17, wherein the operation of determining the assetcorresponding to each daily keyword comprises an operation of storingthe second period as relevance information of each daily keyword to thecorresponding asset, wherein the computer program further comprises anoperation of transmitting investment guidance on the corresponding assetto a user terminal based on the relevance information when it isdetermined that the same keyword as the any one of the daily keywords ofthe first date is included in the daily keywords of the second date. 21.The apparatus of claim 17, wherein the operation of determining theasset corresponding to each daily keyword comprises an operation ofstoring a daily price change which is equal to or greater than thethreshold value as relevance information of each daily keyword to thecorresponding asset, wherein the computer program further comprises anoperation of transmitting investment guidance on the corresponding assetto a user terminal based on the relevance information when it isdetermined that the same keyword as the any one of the daily keywords ofthe first date is included in the daily keywords of the second date. 22.The apparatus of claim 16, wherein the computer program furthercomprises an operation of, when receiving information about a selecteddaily keyword from a user terminal, transmitting information about anasset corresponding to the selected daily keyword to the user terminal.23. The apparatus of claim 16, wherein the computer program furthercomprises: an operation of, when receiving information about an assetselected by a user from a user terminal, extracting a keywordcorresponding to the selected asset; and an operation of transmittingthe keyword corresponding to the selected asset to the user terminal.24. The apparatus of claim 16, wherein the storage unit stores an assetselected by a user in advance among the pre-registered assets, and thecomputer program further comprises an operation of generating one ormore daily keywords of a second date, an operation of determiningwhether the same keyword as any one of the daily keywords of the firstdate is included in the daily keywords of the second date, and anoperation of transmitting a keyword corresponding to the asset selectedby the user to a user terminal when it is determined that the samekeyword as the any one of the daily keywords of the first date isincluded in the daily keywords of the second date.
 25. An apparatus forevaluating the relevance of a keyword to an asset price, the apparatuscomprising: one or more processors; a memory which loads a computerprogram executed by the processors; and a storage unit which storesdaily price information of each pre-registered asset and daily keywordsgenerated by the execution of the computer program, wherein the computerprogram comprises: an operation of identifying an asset whose dailyprice change during a first period is equal to or greater than athreshold value among the pre-registered assets; an operation ofcollecting text content posted on a first date through the Internet; anoperation of generating one or more daily keywords of the first date byextracting a keyword from each piece of the text content; an operationof detecting daily appearance frequency of each daily keyword of thefirst date during a second period; an operation of extracting a keywordwhose daily appearance frequency during the second period corresponds tothe daily price change of the identified asset during the first periodfrom the daily keywords of the first date; and an operation ofdetermining the extracted keyword to be a keyword corresponding to theidentified asset.
 26. The apparatus of claim 25, further comprising anetwork interface which transmits the determined keyword, wherein thecomputer program comprises an operation of detecting a price change ofthe identified asset which is equal to or greater than the thresholdvalue during a third period, an operation of identifying an assetcorresponding to the determined keyword among the pre-registered assets,and an operation of transmitting investment guidance on the identifiedasset to a user terminal.
 27. A method of displaying asset informationmatched with text content by a user terminal, the method comprising:displaying text content in a first area of a display unit of the userterminal; extracting one or more keywords from the text content;extracting an asset matched with each of the extracted keywords frompre-registered assets; and displaying price information of an assetwhich has been extracted a preset number of times or more in a secondarea different from the first area when the asset which has beenextracted the preset number of times or more is included in theextracted assets.
 28. The method of claim 27, wherein the asset matchedwith each of the extracted keywords comprises an asset whose daily pricechange during a second period is equal to or greater than a thresholdvalue in response to daily appearance frequency of each of the extractedkeywords during a first period, and the price information comprisesprediction information about the price of the asset which has beenextracted the preset number of times or more, wherein the predictioninformation about the price of the asset which has been extracted thepreset number of times or more is determined based on relevanceinformation of each of the extracted keywords to the asset which hasbeen extracted the preset number of times or more.
 29. The method ofclaim 27, wherein the extracting of the asset matched with each of theextracted keywords comprises, when the asset which has been extractedthe preset number of times or more is included in the extracted assets,matching the asset which has been extracted the preset number of timesor more with the text content.
 30. A method of displaying assetinformation matched with text content by a user terminal, the methodcomprising: displaying information about an asset in a first area of adisplay unit of the user terminal; displaying a list of pieces of textcontent matched with the asset in a second area different from the firstarea; and when any one of the pieces of the text content is selected,displaying the selected piece of the text content, wherein each piece ofthe text content matched with the asset comprises at least one keywordmatched with the asset.